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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-spatialrisk 0.7.3
Propagated dependencies: r-viridis@0.6.5 r-units@1.0-0 r-tmap@4.2 r-terra@1.8-86 r-sf@1.0-23 r-rlang@1.1.6 r-rcppprogress@0.4.2 r-rcpp@1.1.0 r-mapview@2.11.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-fs@1.6.6 r-dplyr@1.1.4 r-data-table@1.17.8 r-classint@0.4-11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mharinga/spatialrisk
Licenses: GPL 2+
Build system: r
Synopsis: Calculating Spatial Risk
Description:

This package provides methods for spatial risk calculations, focusing on efficient determination of the sum of observations within a circle of a given radius. These methods are particularly relevant for applications such as insurance, where recent European Commission regulations require the calculation of the maximum insured value of fire risk policies for all buildings that are partly or fully located within a 200 m radius. The underlying problem is described by Church (1974) <doi:10.1007/BF01942293>.

r-simodels 0.2.0
Propagated dependencies: r-sf@1.0-23 r-rlang@1.1.6 r-od@0.5.1 r-geodist@0.1.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/robinlovelace/simodels
Licenses: AGPL 3+
Build system: r
Synopsis: Flexible Framework for Developing Spatial Interaction Models
Description:

Develop spatial interaction models (SIMs). SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.

r-smoothsurv 2.6
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://msekce.karlin.mff.cuni.cz/~komarek/
Licenses: GPL 2+
Build system: r
Synopsis: Survival Regression with Smoothed Error Distribution
Description:

Contains, as a main contribution, a function to fit a regression model with possibly right, left or interval censored observations and with the error distribution expressed as a mixture of G-splines. Core part of the computation is done in compiled C++ written using the Scythe Statistical Library Version 0.3.

r-slouch 2.1.5
Propagated dependencies: r-memoise@2.0.1 r-crayon@1.5.3 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/kopperud/slouch
Licenses: GPL 2
Build system: r
Synopsis: Stochastic Linear Ornstein-Uhlenbeck Comparative Hypotheses
Description:

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

r-simulist 0.7.0
Propagated dependencies: r-rlang@1.1.6 r-randomnames@1.6-0.0 r-grates@1.7.1 r-epiparameter@0.4.1 r-english@1.2-6 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/epiverse-trace/simulist
Licenses: Expat
Build system: r
Synopsis: Simulate Disease Outbreak Line List and Contacts Data
Description:

This package provides tools to simulate realistic raw case data for an epidemic in the form of line lists and contacts using a branching process. Simulated outbreaks are parameterised with epidemiological parameters and can have age-structured populations, age-stratified hospitalisation and death risk and time-varying case fatality risk.

r-simphony 1.0.3
Propagated dependencies: r-foreach@1.5.2 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://simphony.hugheylab.org
Licenses: GPL 2
Build system: r
Synopsis: Simulating Large-Scale, Rhythmic Data
Description:

This package provides a tool for simulating rhythmic data: transcriptome data using Gaussian or negative binomial distributions, and behavioral activity data using Bernoulli or Poisson distributions. See Singer et al. (2019) <doi:10.7717/peerj.6985>.

r-steppedwedge 1.0.0
Propagated dependencies: r-stringr@1.6.0 r-performance@0.15.2 r-magrittr@2.0.4 r-lme4@1.1-37 r-ggplot2@4.0.1 r-geepack@1.3.13 r-forcats@1.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://avi-kenny.github.io/steppedwedge/
Licenses: GPL 3
Build system: r
Synopsis: Analyze Data from Stepped Wedge Cluster Randomized Trials
Description:

Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", <doi:10.1002/sim.9511>.

r-simplephenotypes 1.3.0
Propagated dependencies: r-snprelate@1.44.0 r-mvtnorm@1.3-3 r-gdsfmt@1.46.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/samuelbfernandes/simplePHENOTYPES
Licenses: Expat
Build system: r
Synopsis: Simulation of Pleiotropic, Linked and Epistatic Phenotypes
Description:

The number of studies involving correlated traits and the availability of tools to handle this type of data has increased considerably in the last decade. With such a demand, we need tools for testing hypotheses related to single and multi-trait (correlated) phenotypes based on many genetic settings. Thus, we implemented various options for simulation of pleiotropy and Linkage Disequilibrium under additive, dominance and epistatic models. The simulation currently takes a marker data set as an input and then uses it for simulating multiple traits as described in Fernandes and Lipka (2020) <doi:10.1186/s12859-020-03804-y>.

r-spcp 1.4.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-msm@1.8.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spCP
Licenses: GPL 2+
Build system: r
Synopsis: Spatially Varying Change Points
Description:

This package implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

r-stpga 5.2.1
Propagated dependencies: r-scatterplot3d@0.3-44 r-scales@1.4.0 r-emoa@0.5-3 r-algdesign@1.2.1.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=STPGA
Licenses: GPL 3
Build system: r
Synopsis: Selection of Training Populations by Genetic Algorithm
Description:

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.

r-support 0.1.7
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=support
Licenses: GPL 2+
Build system: r
Synopsis: Support Points
Description:

The functions sp() and sp_seq() compute the support points in Mak and Joseph (2018) <DOI:10.1214/17-AOS1629>. Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642.

r-seer 1.1.8
Propagated dependencies: r-urca@1.3-4 r-tsfeatures@1.1.1 r-tibble@3.3.0 r-stringr@1.6.0 r-randomforest@4.7-1.2 r-purrr@1.2.0 r-magrittr@2.0.4 r-future@1.68.0 r-furrr@0.3.1 r-forectheta@3.0 r-forecast@8.24.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://thiyangt.github.io/seer/
Licenses: GPL 3
Build system: r
Synopsis: Feature-Based Forecast Model Selection
Description:

This package provides a novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. seer package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.

r-sigmajs 0.1.5
Propagated dependencies: r-shiny@1.11.1 r-scales@1.4.0 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-igraph@2.2.1 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-dplyr@1.1.4 r-crosstalk@1.2.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://sigmajs.john-coene.com/
Licenses: Expat
Build system: r
Synopsis: Interface to 'Sigma.js' Graph Visualization Library
Description:

Interface to sigma.js graph visualization library including animations, plugins and shiny proxies.

r-shinymergely 0.2.0
Propagated dependencies: r-shiny@1.11.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/stla/shinyMergely
Licenses: GPL 3
Build system: r
Synopsis: Compare and Merge Two Files with a 'Shiny' App
Description:

This package provides a Shiny app allowing to compare and merge two files, with syntax highlighting for several coding languages.

r-sad 0.1.3
Propagated dependencies: r-emdist@0.3-3 r-dualtrees@0.1.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sad
Licenses: Expat
Build system: r
Synopsis: Verify the Scale, Anisotropy and Direction of Weather Forecasts
Description:

Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.

r-shrinktvpvar 1.0.1
Propagated dependencies: r-zoo@1.8-14 r-stochvol@3.2.8 r-shrinktvp@3.1.1 r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-mvtnorm@1.3-3 r-lattice@0.22-7 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shrinkTVPVAR
Licenses: GPL 2+
Build system: r
Synopsis: Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage
Description:

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.

r-samplesizediagnostics 0.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SampleSizeDiagnostics
Licenses: GPL 3
Build system: r
Synopsis: Choosing Sample Size for Evaluating a Diagnostic Test
Description:

Calculates the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision. Based on Buderer (1996) <doi:10.1111/j.1553-2712.1996.tb03538.x>.

r-simmulticorrdata 0.2.2
Propagated dependencies: r-vgam@1.1-13 r-triangle@1.0 r-psych@2.5.6 r-nleqslv@3.3.5 r-matrix@1.7-4 r-ggplot2@4.0.1 r-genord@2.0.0 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/AFialkowski/SimMultiCorrData
Licenses: GPL 2
Build system: r
Synopsis: Simulation of Correlated Data with Multiple Variable Types
Description:

Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order (<DOI:10.1007/BF02293811>) or Headrick's fifth-order (<DOI:10.1016/S0167-9473(02)00072-5>) polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, <DOI:10.1002/asmb.901>). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, <DOI:10.1002/asmb.2072>). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.

r-simtimer 4.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://github.com/ims-fhs/simtimer
Licenses: GPL 3
Build system: r
Synopsis: Datetimes as Integers for Discrete-Event Simulations
Description:

Handles datetimes as integers for the usage inside Discrete-Event Simulations (DES). The conversion is made using the internally generic function as.numeric() of the base package. DES is described in Simulation Modeling and Analysis by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.

r-sgmodel 0.1.2
Propagated dependencies: r-rtauchen@1.0 r-ramify@0.4.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sgmodel
Licenses: GPL 3
Build system: r
Synopsis: Solves a Generic Stochastic Growth Model with a Representative Agent
Description:

It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes the solutions to the model, plots of the solution, a summary of the features of the model, a function that covers different types of consumption preferences, and a function that computes the moments of a Markov process. Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>, Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>, Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).

r-sgof 2.3.5
Propagated dependencies: r-poibin@1.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sgof
Licenses: GPL 2
Build system: r
Synopsis: Multiple Hypothesis Testing
Description:

Seven different methods for multiple testing problems. The SGoF-type methods (see for example, Carvajal Rodrà guez et al., 2009 <doi:10.1186/1471-2105-10-209>; de Uña à lvarez, 2012 <doi:10.1515/1544-6115.1812>; Castro Conde et al., 2015 <doi:10.1177/0962280215597580>) and the BH and BY false discovery rate controlling procedures.

r-spte2m 1.0.3
Propagated dependencies: r-rmarkdown@2.30 r-mass@7.3-65 r-maps@3.4.3 r-mapproj@1.2.12 r-knitr@1.50 r-glmnet@4.1-10 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpTe2M
Licenses: GPL 3+
Build system: r
Synopsis: Nonparametric Modeling and Monitoring of Spatio-Temporal Data
Description:

Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.

r-survma 1.6.8
Propagated dependencies: r-survival@3.8-3 r-quadprog@1.5-8 r-pec@2025.06.24 r-maxlik@1.5-2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: <https://github.com/Stat-WangXG/SurvMA>
Licenses: GPL 2+
Build system: r
Synopsis: Model Averaging Prediction of Personalized Survival Probabilities
Description:

Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) <doi:10.1016/j.csda.2023.107759>.

r-sfo 0.1.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sfo
Licenses: Expat
Build system: r
Synopsis: San Francisco International Airport Monthly Air Passengers
Description:

This package provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc. Data source: San Francisco data portal (DataSF) <https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.

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Total results: 68658